import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PlateAnalysis_Echo import PlateDataset
from tqdm import tqdm

dataset_0 = PlateDataset('20191105_Dopt_2.CSV','D_Opt.csv')
dataset_1 = PlateDataset('20191105_Dopt_2_Shaking.CSV','D_Opt.csv')
dataset_2 = PlateDataset('20191105_Dopt_2_Shaking_rep2.CSV','D_Opt.csv')
dataset_3 = PlateDataset('20191105_Dopt_2_Shaking_rep3.CSV','D_Opt.csv')


output =  pd.DataFrame([],columns = [ 'K','percentDMSO', 'Vol','km', 'vmax', 'loss', 'r_squared'])
for i in tqdm(range(1,19)):
    output=output.append(dataset_1.CalculateMetrics(i))
    output=output.append(dataset_2.CalculateMetrics(i))
    output=output.append(dataset_3.CalculateMetrics(i))
    
output = output.reset_index(drop=True)
output.to_csv('DOptExperimnentResults.csv')
output

100%|██████████| 18/18 [00:42<00:00,  2.29s/it]

Rep1_NoShake =  pd.DataFrame([],columns = [ 'K','percentDMSO', 'Vol','km', 'vmax', 'loss', 'r_squared'])
Rep1_Shake =  pd.DataFrame([],columns = [ 'K','percentDMSO', 'Vol','km', 'vmax', 'loss', 'r_squared'])

for i in tqdm(range(1,19)):
    Rep1_NoShake=Rep1_NoShake.append(dataset_0.CalculateMetrics(i))
    Rep1_Shake=Rep1_Shake.append(dataset_1.CalculateMetrics(i))
    
Rep1_NoShake.reset_index(inplace=True,drop=True)
Rep1_Shake.reset_index(inplace=True,drop=True)

MixingDifference = Rep1_Shake['r_squared'] - Rep1_NoShake['r_squared']
MixingDifference.name = 'Difference in R sq'

pd.concat([Rep1_Shake[['K','percentDMSO','Vol']],MixingDifference],axis=1)

100%|██████████| 18/18 [00:26<00:00,  1.56s/it]

for i in range(1,19):
    print('Rep 1')
    dataset_1.PlotFigure(i)
    print('Rep 2')
    dataset_2.PlotFigure(i)
    print('Rep 3')
    dataset_3.PlotFigure(i)

Rep 1

Rep 2

Rep 3

Rep 1

Rep 2

Rep 3

Rep 1

Rep 2

Rep 3

Rep 1

from sklearn.decomposition import PCA

pca = PCA(n_components=2)

decomp = pca.fit_transform(dataset_3.data)

plt.scatter(decomp[:,0],decomp[:,1])
plt.show()

plt.figure(figsize=(20,5))
for i in dataset_1.data.index:
    plt.plot(dataset_1.data.loc[i,:],alpha = 0.2)
plt.xticks(range(220,800,10))
plt.ylim((0,0.5))
plt.xlim(300,800)
plt.show()

dataset_1

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-7f6531e28fd8> in <module>
----> 1 dataset_1.Diff(data)

AttributeError: 'PlateDataset' object has no attribute 'Diff'

	K	percentDMSO	Vol	km	vmax	loss	r_squared
0	1.000000	5.000000	20.0	2.675369	0.047003	0.012980	0.328103
1	1.000000	5.000000	20.0	2.227608	0.027246	0.007528	0.517799
2	1.000000	5.000000	20.0	2.581205	0.045789	0.015981	0.368032
3	2.636364	3.181818	20.0	2.475195	0.031945	0.005781	0.691983
4	2.636364	3.181818	20.0	2.109391	0.039754	0.003395	0.885325
5	2.636364	3.181818	20.0	2.036853	0.033326	0.007022	0.676865
6	4.000000	1.000000	20.0	0.977021	0.058728	0.001813	0.990724
7	4.000000	1.000000	20.0	2.257035	0.061204	0.007720	0.816803
8	4.000000	1.000000	20.0	0.484916	0.048700	0.001620	0.986696
9	4.000000	1.000000	20.0	2.547141	0.052887	0.003677	0.935980
10	4.000000	1.000000	20.0	1.865010	0.057868	0.009088	0.471061
11	4.000000	1.000000	20.0	0.125037	0.048281	0.006071	0.768513
12	1.545455	1.000000	20.0	2.483997	0.036234	0.012994	0.312070
13	1.545455	1.000000	20.0	2.713912	0.050843	0.015150	0.252104
14	1.545455	1.000000	20.0	2.740432	0.054430	0.015246	0.315840
15	1.000000	5.000000	20.0	1.932250	0.010229	0.012775	-0.077052
16	1.000000	5.000000	20.0	2.180274	0.022834	0.009380	0.284250
17	1.000000	5.000000	20.0	1.984917	0.016148	0.010116	0.165649
18	2.636364	3.181818	20.0	-1.569767	0.049392	0.004332	0.998290
19	2.636364	3.181818	20.0	-0.683843	0.049637	0.031566	0.160381
20	2.636364	3.181818	20.0	-0.300910	0.051674	0.026682	0.079612
21	2.636364	5.000000	29.0	2.824726	0.048840	0.003134	0.885150
22	2.636364	5.000000	29.0	2.779152	0.050375	0.011771	0.335623
23	2.636364	5.000000	29.0	2.352918	0.047634	0.009516	0.314454
24	2.636364	5.000000	29.0	3.372817	0.074971	0.007911	0.837700
25	2.636364	5.000000	29.0	3.093418	0.073908	0.010997	0.765327
26	2.636364	5.000000	29.0	3.089719	0.073567	0.009030	0.745127
27	1.000000	2.818182	29.0	2.759980	0.053463	0.018398	0.117035
28	1.000000	2.818182	29.0	2.895809	0.066171	0.020328	-0.085152
29	1.000000	2.818182	29.0	3.016501	0.066801	0.020774	-0.128463
30	1.000000	2.818182	29.0	2.403532	0.077447	0.002035	0.972183
31	1.000000	2.818182	29.0	2.438965	0.079011	0.001350	0.988732
32	1.000000	2.818182	29.0	2.441895	0.078780	0.002889	0.967037
33	1.000000	5.000000	40.0	2.911388	0.071689	0.009754	0.382532
34	1.000000	5.000000	40.0	3.082599	0.069612	0.010115	0.392385
35	1.000000	5.000000	40.0	3.094629	0.069797	0.011311	0.333080
36	1.000000	1.000000	40.0	1.468051	0.115580	0.004583	0.977210
37	1.000000	1.000000	40.0	3.741961	0.126283	0.002852	0.991068
38	1.000000	1.000000	40.0	3.486001	0.119758	0.002842	0.988892
39	4.000000	5.000000	40.0	1.446555	0.110717	0.004600	0.978295
40	4.000000	5.000000	40.0	1.523311	0.108327	0.004352	0.982013
41	4.000000	5.000000	40.0	1.123542	0.098365	0.003310	0.987439
42	1.000000	5.000000	40.0	3.162315	0.075010	0.027382	0.089503
43	1.000000	5.000000	40.0	3.152601	0.072772	0.024214	0.037659
44	1.000000	5.000000	40.0	3.102813	0.070091	0.027736	0.106447
45	1.000000	1.000000	40.0	2.310959	0.031861	0.027076	0.113705
46	1.000000	1.000000	40.0	2.298626	0.031986	0.022425	0.171065
47	1.000000	1.000000	40.0	2.260342	0.028294	0.024612	0.071522
48	4.000000	1.000000	40.0	0.035702	0.110071	0.004118	0.972540
49	4.000000	1.000000	40.0	0.024896	0.108735	0.003161	0.974311
50	4.000000	1.000000	40.0	0.180984	0.099658	0.005794	0.873542
51	4.000000	1.000000	40.0	0.970282	0.103618	0.003518	0.980284
52	4.000000	1.000000	40.0	0.448479	0.091980	0.003289	0.987029
53	4.000000	1.000000	40.0	0.963389	0.099711	0.003788	0.983885

	K	percentDMSO	Vol	Difference in R sq
0	1.000000	5.000000	20.0	0.068145
1	2.636364	3.181818	20.0	0.229469
2	4.000000	1.000000	20.0	0.063834
3	4.000000	1.000000	20.0	0.034579
4	1.545455	1.000000	20.0	0.056007
5	1.000000	5.000000	20.0	-0.032856
6	2.636364	3.181818	20.0	1.009708
7	2.636364	5.000000	29.0	-0.001880
8	2.636364	5.000000	29.0	0.053933
9	1.000000	2.818182	29.0	-0.083629
10	1.000000	2.818182	29.0	-0.001108
11	1.000000	5.000000	40.0	-0.017544
12	1.000000	1.000000	40.0	-0.001960
13	4.000000	5.000000	40.0	-0.003488
14	1.000000	5.000000	40.0	-0.012591
15	1.000000	1.000000	40.0	-0.004671
16	4.000000	1.000000	40.0	0.007028
17	4.000000	1.000000	40.0	-0.003935